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Machine Learning

Machine learning is increasingly making way in all kinds of industries.<br>Manufacturers are more interested in finding new ways to grow, enlarge<br>product quality, and at the same time make short production runs with<br>customers. New business models often bring with them the paradox of new<br>product lines that affect existing ERP, CRM, and PLM(Product lifecycle<br>management) systems, as customer performance needs to be constantly<br>improved.

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Machine Learning

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  1. Binary Informatics Reasons Why Big Companies Are Opting for Machine Learning Machine Learning Machine learning is increasingly making way in all kinds of industries. Manufacturers are more interested in finding new ways to grow, enlarge product quality, and at the same time make short production runs with customers. New business models often bring with them the paradox of new product lines that affect existing ERP, CRM, and PLM(Product lifecycle management) systems, as customer performance needs to be constantly improved. Here are 5 ways people are adopting 1 1. .S Se em mi ic co on nd du uc ct to or r O Ou ut tp pu ut t I Im mp pr ro ov ve em me en nt t The improved semiconductor output power is up to 30%, which reduces the rejection rate and optimizes the manufacturing steps that can be achieved with ML. 2 2. .I In nv ve en nt to or ry y t tr ra ac ck ki in ng g, , S Su up pp pl ly y c ch ha ai in n v vi is si ib bi il li it ty y, , a an nd d I In nv ve en nt to or ry y o op pt ti im mi iz za at ti io on n Asset Management, Supply Chain Management, and Inventory Management are the key areas of artificial intelligence, machine learning, and the Internet of things in today’s manufacturing. 3 3. .R Re ed du uc ce ed d F Fo or re ec ca as st ti in ng g E Er rr ro or r McKinsey predicts that machine learning will reduce forecasting errors in the supply chain by 50 percent and reduce revenue losses by 65 percent through improved product availability. Supply chains are the soul of every manufacturing company. 4 4. .A Au ut to om ma at ti in ng g i in nv ve en nt to or ry y Automating inventory optimization through machine learning has improved service levels by 16 percent and increased inventory performance by 25 percent. 5 5. .R Re ed du uc ce ed d T Te es st ti in ng g a an nd d C Ca al li ib br ra at ti io on n T Ti im me e One manufacturer reduced test and calibration time by 35% by accurately predicting the calibration and test results through ML. The aim of the project was to reduce the testing and calibration time in the production of mobile hydraulic pumps.

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